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aslan-ng/lora-green-patents

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Model Card for aslan-ng/lora-green-patents

This model classifies patents and product descriptions as green (eco-friendly) or not green. It was finetuned with LoRA on a binary text dataset of patent descriptions.

Model Details

Model Description

  • —Developed by: Jennifer Evans, Aslan Noorghasemi
  • —Model type: Text classifier (binary classification)
  • —Languages (NLP): English
  • —Finetuned from model: LoRA with the "distilbert-base-uncased" model and DistilBertForSequenceClassification
  • —

Model Sources

  • —Training Dataset: https://huggingface.co/datasets/cwinkler/patentsgreenplastics
  • —Model Details
  • —Train/test split: 80/20
  • —LoRA alpha: 16
  • —LoRA dropout: 0.1
  • —Eval steps: 500
  • —Learning rate: 2e-4
  • —Training epochs: 10

Uses

Direct Use

Use this model to classify whether input text is considered green (eco-friendly) or not. It takes patent or product descriptions as text inputs and returns a predicted binary label and probabilities.

Downstream Use

It can be incorporated into larger text evaluation systems (ie. patent and product analysis tasks) as a pre-screening classifier.

Out-of-Scope Use

Not intended for:

  • —Safety-critical deployment without further validation.
  • —Identifying other labels beyond green / not green.
  • —Applications outside of evaluating patent and product descriptions.

Bias, Risks, and Limitations

The model is trained on a specific dataset. It may:

  • —Misclassify unusual or unclear eco-friendly descriptions.
  • —Perform poorly on non-U.S. descriptions if not present in training.
  • —Inherit any biases in the training text.

Recommendations

Always test on your target data before deployment. Combine with additional checks in safety-critical scenarios.

How to Get Started with the Model

The model can be used by loading both the base and adaptor

  • —Model Name: "distilbert-base-uncased"
  • —Tokenizer: AutoTokenizer.frompretrained(REPOIDLORAGREEN_PATENTS)
  • —Base Model: AutoModelForSequenceClassification.frompretrained(modelname, num_labels=2)
  • —Model: PeftModel.frompretrained(basemodel, REPOIDLORAGREENPATENTS)